Experimental results on synthetic data and real-world data at different scales –namely, Telegram [1]}, Blog [2]},
Migration [3]}, and
WikiTalk [4]}, demonstrate that our method can achieve state-of-the-art performance for a wide range of network densities and topologies. Compared to its competitors, for synthetic data, our method achieves superior performance
(with respect to the Adjusted Rand Index (ARI) [5]});
for the real-world data, our experimental results indicate that our method outperforms them,
using imbalance scores as outcome measures.
We also apply our loss function to the tasks of node classification and link direction prediction,
and witness a modest average gain in accuracy
on the benchmark data sets Cora-ML and CiteSeer [6]}.
